In Open forum infectious diseases
Background : Medically vulnerable individuals are at increased risk of acquiring multidrug-resistant Enterobacterales (MDR-E) infections. People with HIV (PWH) experience a greater burden of comorbidities and may be more susceptible to MDR-E due to HIV-specific factors.
Methods : We performed an observational study of PWH participating in an HIV clinical cohort and engaged in care at a tertiary care center in the Southeastern United States from 2000 to 2018. We evaluated demographic and clinical predictors of MDR-E by estimating prevalence ratios (PRs) and employing machine learning classification algorithms. In addition, we created a predictive model to estimate risk of MDR-E among PWH using a machine learning approach.
Results : Among 4734 study participants, MDR-E was isolated from 1.6% (95% CI, 1.2%-2.1%). In unadjusted analyses, MDR-E was strongly associated with nadir CD4 cell count ≤200 cells/mm3 (PR, 4.0; 95% CI, 2.3-7.4), history of an AIDS-defining clinical condition (PR, 3.7; 95% CI, 2.3-6.2), and hospital admission in the prior 12 months (PR, 5.0; 95% CI, 3.2-7.9). With all variables included in machine learning algorithms, the most important clinical predictors of MDR-E were hospitalization, history of renal disease, history of an AIDS-defining clinical condition, CD4 cell count nadir ≤200 cells/mm3, and current CD4 cell count 201-500 cells/mm3. Female gender was the most important demographic predictor.
Conclusions : PWH are at risk for MDR-E infection due to HIV-specific factors, in addition to established risk factors. Early HIV diagnosis, linkage to care, and antiretroviral therapy to prevent immunosuppression, comorbidities, and coinfections protect against antimicrobial-resistant bacterial infections.
Henderson Heather I, Napravnik Sonia, Kosorok Michael R, Gower Emily W, Kinlaw Alan C, Aiello Allison E, Williams Billy, Wohl David A, van Duin David
2022-Oct
Enterobacterales, HIV, gram-negative, machine learning, multidrug resistance
